PREPROCESSING SEQUENCES

Script to process ITS sequences from LMNP and IPNP

IMPORTING AND DEMULTIPLEXING SEQUENCES

IMPORTING TO QIIME2

Make directories for each library

mkdir ITS1
mkdir ITS2
mkdir ITS3
mkdir ITS4
mkdir ITS5
mkdir ITS6
mkdir ITS7
mkdir ITS8
mkdir ITS9

Loop to rename forward seqs

for i in ITS* ;do mv ITS*/ITS_*1.fastq.gz  ITS*/forward.fastq.gz ; done

Loop to rename reverse seqs

for i in ITS* ;do mv ITS*/ITS_*2.fastq.gz  ITS*/reverse.fastq.gz ; done

Loop to import files to QIIME2

for i in ITS*; do qiime tools import --type MultiplexedPairedEndBarcodeInSequence --input-path $i --output-path $i.qza ; done

DEMULTIPLEXING WITH CUTADAPT

qiime cutadapt demux-paired\
  --i-seqs its1.qza\
  --m-forward-barcodes-file ../../maps_its/its1.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its1_demux

qiime cutadapt demux-paired\
  --i-seqs its2.qza\
  --m-forward-barcodes-file ../../maps_its/its2.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its2_demux

qiime cutadapt demux-paired\
  --i-seqs its3.qza\
  --m-forward-barcodes-file ../../maps_its/its3.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its3_demux

qiime cutadapt demux-paired\
  --i-seqs its4.qza --m-forward-barcodes-file ../../maps_its/its4.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its4_demux

qiime cutadapt demux-paired  --i-seqs its5.qza\
  --m-forward-barcodes-file ../../maps_its/its5.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its5_demux

qiime cutadapt demux-paired  --i-seqs its6.qza\
  --m-forward-barcodes-file ../../maps_its/its6.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its6_demux

qiime cutadapt demux-paired  --i-seqs its7.qza\
  --m-forward-barcodes-file ../../maps_its/its7.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its7_demux

qiime cutadapt demux-paired\
  --i-seqs its8.qza\
  --m-forward-barcodes-file ../../maps_its/its8.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its8_demux

qiime cutadapt demux-paired\
  --i-seqs its9.qza\
  --m-forward-barcodes-file ../../maps_its/its9.txt\
  --m-forward-barcodes-column BarcodeSequence\
  --output-dir its9_demux

ITSXPRESS QIIME2

OPTION 1: MERGED SEQUENCES

Running itsxpress

for i in its*_demux ; do qiime itsxpress trim-pair --i-per-sample-sequences $i/per_sample_sequences.qza --p-region ITS2 --p-taxa F --o-trimmed itsxpress_2024_$i;done

Export sequences

for i in itsxpress_2024_its*; do qiime tools export --input-path $i --output-path exported; done

Import sequences together

qiime tools import  --type 'SampleData[SequencesWithQuality]'  --input-path manifest.txt  --output-path single-end-demux-qiime2.qza  --input-format SingleEndFastqManifestPhred33V2

OPTION 2: UNMERGED SEQUENCES

Running itsxpress

for i in its*_demux ; do qiime itsxpress trim-pair-output-unmerged --i-per-sample-sequences $i/per_sample_sequences.qza --p-region ITS2 --p-taxa F --o-trimmed itsxpress_2024_unmerged$i;done

Export sequences

for i in itsxpress_2024_unmerged*; do qiime tools export --input-path $i --output-path exported_$i; done

Import sequences together

 qiime tools import   --type 'SampleData[PairedEndSequencesWithQuality]'   --input-path manifest.txt   --output-path paired-end-demux.qza   --input-format PairedEndFastqManifestPhred33V2

ITSXPRESS STANDALONE

EXPORTING DATA

for i in its*_demux; do qiime tools export  --input-path  $i/per_sample_sequences.qza --output-path  exported_$i; done

OPTION 1 : MERGED SEQUENCES

Move all data to one directory and run this bash script (chatGPT):


#!/bin/bash

# Set the path to the itsxpress executable
ITSXPRESS_EXECUTABLE="itsxpress"

# Loop through the files
for forward_file in *_R1_001.fastq.gz; do
    # Extract the sample name from the forward file
    sample_name=$(basename "$forward_file" _R1_001.fastq.gz)

    # Construct the reverse file name
    reverse_file="${sample_name}_R2_001.fastq.gz"

    # Run itsxpress command
    $ITSXPRESS_EXECUTABLE --fastq "$forward_file" --fastq2 "$reverse_file" --region ITS2 \
        --taxa Fungi --log "${sample_name}_logfile.txt" --outfile "${sample_name}_trimmed_reads.fastq.gz" --threads 2

    # Optionally, you can print a message indicating the completion of each iteration
    echo "Processing $forward_file and $reverse_file"
done

OPTION 2 : UNMERGED SEQUENCES

Move all data to one directory and run this bash script (chatGPT):


#!/bin/bash

# Set the path to the itsxpress executable
ITSXPRESS_EXECUTABLE="itsxpress"

# Loop through the files
for forward_file in *_R1_001.fastq.gz; do
    # Extract the sample name from the forward file
    sample_name=$(basename "$forward_file" _R1_001.fastq.gz)

    # Construct the reverse file name
    reverse_file="${sample_name}_R2_001.fastq.gz"

    # Run itsxpress command
    $ITSXPRESS_EXECUTABLE --fastq "$forward_file" --fastq2 "$reverse_file" --region ITS2 \
        --taxa Fungi --log "${sample_name}_logfile.txt" --outfile "${sample_name}_trimmed_R1.fastq.gz" --outfile2 "${sample_name}_trimmed_R2.fastq.gz" --threads 4

    # Optionally, you can print a message indicating the completion of each iteration
    echo "Processing $forward_file and $reverse_file"
done

DADA2 AND OTUS IN QIIME2

IMPORTING STANDALONE DATA

qiime tools import\
  --type 'SampleData[SequencesWithQuality]'\
  --input-path manifest.txt\
  --output-path single-end-demux-standalone.qza\
  --input-format SingleEndFastqManifestPhred33V2

DADA2 FOR MERGED SEQUENCES

Run DADA2 on the two data-sets.

 for i in *; do qiime dada2 denoise-single --i-demultiplexed-seqs $i --p-trunc-len 0 --p-n-threads 4 --output-dir dada2_$i; done

Filter seqs by length (>50bp)

qiime feature-table filter-seqs\
  --m-metadata-file dada2_single-end-demux-qiime2.qza/representative_sequences.qza\
  --i-data dada2_single-end-demux-qiime2.qza/representative_sequences.qza\
  --o-filtered-data representative_sequences_50_qiime2.qza\
  --p-where "length(sequence) > 49"

qiime feature-table filter-seqs\
  --m-metadata-file dada2_single-end-demux-standalone.qza/representative_sequences.qza\
  --i-data dada2_single-end-demux-standalone.qza/representative_sequences.qza\
  --o-filtered-data representative_sequences_50_standalone.qza\
  --p-where "length(sequence) > 49"

Filter table based on seqs

qiime feature-table filter-features\
  --i-table dada2_single-end-demux-qiime2.qza/table.qza\
  --m-metadata-file representative_sequences_50_qiime2.qza\
  --o-filtered-table table_50_qiime2.qza

qiime feature-table filter-features\
  --i-table dada2_single-end-demux-standalone.qza/table.qza\
  --m-metadata-file representative_sequences_50_standalone.qza\
  --o-filtered-table table_50_standalone.qza

DADA2 FOR UNMERGED SEQUENCES

 qiime dada2 denoise-paired --i-demultiplexed-seqs paired-end-demux.qza  --p-trunc-len-f 0 --p-trunc-len-r 0 --p-n-threads 4 --output-dir dada2_paired

Filter by length not neccesary because min length was 231.

DADA2 FOR UNMERGED SEQUENCES STANDALONE ISTXPRESS

qiime tools import   --type 'SampleData[PairedEndSequencesWithQuality]'   --input-path manifest_unmerged.txt   --output
-path paired-end-demux-standalone.qza --input-format PairedEndFastqManifestPhred33V2

qiime dada2 denoise-paired --i-
demultiplexed-seqs paired-end-demux-standalone.qza  --p-trunc-len-f 0 --p-trunc-len-r 0
--p-n-threads 4 --output-dir dada2_paired_standalone

OTU’s FOR MERGED SEQUENCES

For qiime2-itsxpress data, we have to export and run:

for i in *.fastq.gz; do reformat in=$i out=clean_$i minconsecutivebases=1; done

This script is from (BBMap)[https://github.com/BioInfoTools/BBMap/blob/master/sh/reformat.sh]

Then import again and run all. This is due to an error of ITSxpress that generates sequences with 0 lentgh. The next steps will be run for the standalone and qiime2 data in order to cluster to OTUS.

Filter by q-score

for i in *;do  qiime quality-filter q-score --i-demux $i --output-dir filterqscore_$i; done

Derreplication

for i in filterqscore_*; do qiime vsearch dereplicate-sequences --i-sequences $i/filtered_sequences.qza --output-dir derep_$i;done

Clustering de novo

for i in derep_* ; do qiime vsearch cluster-features-de-novo --i-sequences $i/dereplicated_sequences.qza --i-table $i/dereplicated_table.qza --p-perc-identity 0.97 --p-threads 4 --output-dir cluster97_$i; done

Chimera checking and filter from table

for i in cluster97_*; do qiime vsearch uchime-denovo --i-sequences $i/clustered_sequences.qza --i-table $i/clustered_table.qza --output-dir chimera97_$i;done

qiime feature-table filter-features\
  --i-table cluster97_derep_filterqscore_single-end-demux-qiime2-reformat.qza/clustered_table.qza\
  --m-metadata-file chimera97_cluster_derep_filterqscore_single-end-demux-qiime2-reformat.qza/nonchimeras.qza  \
  --o-filtered-table chimera97_cluster_derep_filterqscore_single-end-demux-qiime2-reformat.qza/table97-nonchimeras-qiime2.qza

qiime feature-table filter-features\
--i-table cluster97_derep_filterqscore_single-end-demux-standalone.qza/clustered_table.qza\
--m-metadata-file chimera97_cluster_derep_filterqscore_single-end-demux-standalone.qza/nonchimeras.qza  --o-filtered-table chimera97_cluster_derep_filterqscore_single-end-demux-standalone.qza/table97-nonchimeras-standalone.qza

Filtering singletons from table and seqs (optional)

 for i in chimera* ; do qiime feature-table filter-features --i-table $i/table*.qza --p-min-frequency 2 --o-filtered-table $i/filtered_$i ; done

for i in chimera*/; do
    data_file=$(find $i -name 'nonchimeras*' -type f)
    table_file=$(find $i -name 'filtered-table*' -type f)

    qiime feature-table filter-seqs \
        --i-data $data_file \
        --i-table $table_file \
        --o-filtered-data ${data_file%.qza}-filtered.qza
done

DADA2 IN R

OPTION 1 : MERGED SEQUENCES

Load libraries and check versions

library(dada2)
packageVersion("dada2")
library(ShortRead)
packageVersion("ShortRead")
library(Biostrings)
packageVersion("Biostrings")

Path where the sequences are saved

path <- "../ITS_corredor/HN00174206/data_REANAL_2024_LAST/ITSxpress_STANDALONE/ITSxpress_merged/"  ## CHANGE ME to the directory containing the fastq files.
head(list.files(path))

List of files

fnFs <- sort(list.files(path, pattern = "_L001_trimmed_reads.fastq.gz", full.names = TRUE))

Primers

FWD <- "AACTTTYRRCAAYGGATCWCT"  ## CHANGE ME to your forward primer sequence
REV <- "AGCCTCCGCTTATTGATATGCTTAART"  ## CHANGE ME...

Check for primers (not must be present because itsxpress)

allOrients <- function(primer) {
    # Create all orientations of the input sequence
    require(Biostrings)
    dna <- DNAString(primer)  # The Biostrings works w/ DNAString objects rather than character vectors
    orients <- c(Forward = dna, Complement = Biostrings::complement(dna), Reverse = Biostrings::reverse(dna),
        RevComp = Biostrings::reverseComplement(dna))
    return(sapply(orients, toString))  # Convert back to character vector
}
FWD.orients <- allOrients(FWD)
REV.orients <- allOrients(REV)
FWD.orients

fnFs.filtN <- file.path(path, "filtN", basename(fnFs)) 
#filterAndTrim(fnFs, fnFs.filtN, fnRs, fnRs.filtN, maxN = 0, multithread = TRUE)
filterAndTrim(fnFs, fnFs.filtN, maxN = 0, multithread = TRUE)

primerHits <- function(primer, fn) {
    # Counts number of reads in which the primer is found
    nhits <- vcountPattern(primer, sread(readFastq(fn)), fixed = FALSE)
    return(sum(nhits > 0))
}
rbind(FWD.ForwardReads = sapply(FWD.orients, primerHits, fn = fnFs.filtN[[1]]),
      #FWD.ReverseReads = sapply(FWD.orients,primerHits, fn = fnRs.filtN[[1]]))
      REV.ForwardReads = sapply(REV.orients, primerHits, fn = fnFs.filtN[[1]]))
      #REV.ReverseReads = sapply(REV.orients, primerHits, fn = fnRs.filtN[[1]]))

Quality plot

plotQualityProfile(fnFs[1:2])
#plotQualityProfile(cutRs[1:2])

Filter by quality

filtFs <- file.path(path, "filtered", basename(fnFs))

out <- filterAndTrim(fnFs, filtFs, maxN = 0, maxEE = 2, truncQ = 2,
    minLen = 50, rm.phix = TRUE, compress = TRUE, multithread = TRUE)  # on windows, set multithread = FALSE
head(out)

Model of lear Errors

errF <- learnErrors(filtFs, multithread = TRUE)
#plotErrors(errF, nominalQ = TRUE)

Dereplication

derepFs <- derepFastq(filtFs, verbose = TRUE)

Inference of ASVs

dadaFs <- dada(derepFs, err = errF, multithread = TRUE)
#dadaFs2 <- dada(derepFs, err = errF, multithread = TRUE,   =1e-40, pool="pseudo" )

Sequences table

seqtab <- makeSequenceTable(dadaFs)
#seqtab2 <- makeSequenceTable(dadaFs2)
dim(seqtab)

chimera removal

seqtab_nochim <- removeBimeraDenovo(seqtab, method = "consensus", multithread = T, 
                                    verbose = T)
#seqtab_nochim2 <- removeBimeraDenovo(seqtab2, method = "consensus", multithread = T, 
 #                                   verbose = T)
dim(seqtab_nochim)

Denoising statistics

getN <-function(x) sum(getUniques(x))
# ?getUniques
stats <- cbind(out, sapply(dadaFs, getN), 
               rowSums(seqtab_nochim))

colnames(stats) <- c("input", "filtered", "denoisedF",  "nonchim")

head(stats)

Sequences statistics (chatGPT)

library(tidyverse)
# Obtener los nombres de las columnas de 'df'
seqs <- colnames(seqtab)

# Función para contar letras en una cadena
contar_letras <- function(cadena) {
  str_count(cadena, "[A-Za-z]")
}

# Aplicar la función de conteo a cada elemento de 'nombres_columnas'
conteo_letras <- map_int(seqs, ~ contar_letras(.x))

#print(conteo_letras)
min(conteo_letras)
max(conteo_letras)
mean(conteo_letras)

Assign taxonomy

#----------Asignacion taxonomica-----------
ruta_clasificador <- "../ITS_corredor/HN00174206/data_REANAL_2024_LAST/sh_general_release_dynamic_18.07.2023.fasta" # de la página de UNITE
taxa <- assignTaxonomy(seqtab_nochim, ruta_clasificador, multithread = TRUE, tryRC = TRUE)
taxa2 <- assignTaxonomy(seqtab_nochim2, ruta_clasificador, multithread = TRUE, tryRC = TRUE)

# Visualizar lo que se genero despues de la asignacion
taxa_print <- taxa
rownames(taxa_print) <- NULL

head(taxa_print)
dim(taxa_print)

Export data

save( seqtab_nochim2, taxa2,
      file = "dada2_results2.RData")
write.csv(taxa, "taxonomy.csv")
#write.csv(seqtab_final, "table_final.csv", row.names = TRUE)
write_tsv(seqtab_final %>% rownames_to_column("#SampleID"), "table_final.txt")

write.csv(stats, "stats.csv")

#remotes::install_github("vmikk/metagMisc")
library(metagMisc)
dada_to_fasta(seqtab = seqtab, out = "seqtab.fasta")
#dada_to_fasta(seqtab = seqtab2, out = "seqtab2.fasta")

OPTION 2 : UNMERGED SEQUENCES

Load libraries and check versions

library(dada2)
packageVersion("dada2")
library(ShortRead)
packageVersion("ShortRead")
library(Biostrings)
packageVersion("Biostrings")

Path where the sequences are saved

path <- "../ITS_corredor/HN00174206/data_REANAL_2024_LAST/ITS_STANDALONE/"  ## CHANGE ME to the directory containing the fastq files.
head(list.files(path))

List of files

fnFs <- sort(list.files(path, pattern = "_R1.fastq.gz", full.names = TRUE))
fnRs <- sort(list.files(path, pattern = "_R2.fastq.gz", full.names = TRUE))

Primers

FWD <- "AACTTTYRRCAAYGGATCWCT"  ## CHANGE ME to your forward primer sequence
REV <- "AGCCTCCGCTTATTGATATGCTTAART"  ## CHANGE ME...

Check for primers (not must be present because itsxpress)

allOrients <- function(primer) {
    # Create all orientations of the input sequence
    require(Biostrings)
    dna <- DNAString(primer)  # The Biostrings works w/ DNAString objects rather than character vectors
    orients <- c(Forward = dna, Complement = Biostrings::complement(dna), Reverse = Biostrings::reverse(dna),
        RevComp = Biostrings::reverseComplement(dna))
    return(sapply(orients, toString))  # Convert back to character vector
}
FWD.orients <- allOrients(FWD)
REV.orients <- allOrients(REV)
FWD.orients

fnFs.filtN <- file.path(path, "filtN", basename(fnFs)) # Put N-filtered files in filtN/ subdirectory
fnRs.filtN <- file.path(path, "filtN", basename(fnRs))
filterAndTrim(fnFs, fnFs.filtN, fnRs, fnRs.filtN, maxN = 0, multithread = TRUE)

primerHits <- function(primer, fn) {
    # Counts number of reads in which the primer is found
    nhits <- vcountPattern(primer, sread(readFastq(fn)), fixed = FALSE)
    return(sum(nhits > 0))
}
rbind(FWD.ForwardReads = sapply(FWD.orients, primerHits, fn = fnFs.filtN[[1]]), FWD.ReverseReads = sapply(FWD.orients,
    primerHits, fn = fnRs.filtN[[1]]), REV.ForwardReads = sapply(REV.orients, primerHits,
    fn = fnFs.filtN[[1]]), REV.ReverseReads = sapply(REV.orients, primerHits, fn = fnRs.filtN[[1]]))

remove primers

#cutadapt <- "C:/Users/HP/AppData/Local/Programs/Python/Python312/Scripts/cutadapt.exe" # CHANGE ME to the cutadapt path on your machine
#system2(cutadapt, args = "--version") # Run shell commands from R

# Definir la ruta al lanzador de Python
python_launcher <- "py.exe"

# Ejecutar cutadapt desde R
output <- system2(python_launcher, args = c("-m", "cutadapt", "--version"), stdout = TRUE)
print(output)


path.cut <- file.path(path, "cutadapt")
if(!dir.exists(path.cut)) dir.create(path.cut)
fnFs.cut <- file.path(path.cut, basename(fnFs))
fnRs.cut <- file.path(path.cut, basename(fnRs))

FWD.RC <- dada2:::rc(FWD)
REV.RC <- dada2:::rc(REV)
# Trim FWD and the reverse-complement of REV off of R1 (forward reads)
R1.flags <- paste("-g", FWD, "-a", REV.RC) 
# Trim REV and the reverse-complement of FWD off of R2 (reverse reads)
R2.flags <- paste("-G", REV, "-A", FWD.RC)

# Run Cutadapt
for(i in seq_along(fnFs)) {
  system2(python_launcher, args = c("-m", "cutadapt",R1.flags, R2.flags, "-n", 2, # -n 2 required to remove FWD and REV from reads
                             "-o", fnFs.cut[i], "-p", fnRs.cut[i], # output files
                             fnFs.filtN[i], fnRs.filtN[i],
                             "--minimum-length=1")) # input files
}

rbind(FWD.ForwardReads = sapply(FWD.orients, primerHits, fn = fnFs.cut[[1]]), FWD.ReverseReads = sapply(FWD.orients,
    primerHits, fn = fnRs.cut[[1]]), REV.ForwardReads = sapply(REV.orients, primerHits,
    fn = fnFs.cut[[1]]), REV.ReverseReads = sapply(REV.orients, primerHits, fn = fnRs.cut[[1]]))



# Forward and reverse fastq filenames have the format:
cutFs <- sort(list.files(path.cut, pattern = "_R1.fastq.gz", full.names = TRUE))
cutRs <- sort(list.files(path.cut, pattern = "_R2.fastq.gz", full.names = TRUE))

# Extract sample names, assuming filenames have format:
get.sample.name <- function(fname) strsplit(basename(fname), "_")[[1]][1]
sample.names <- unname(sapply(cutFs, get.sample.name))
head(sample.names)

Quality plot

plotQualityProfile(cutFs[1:2])
plotQualityProfile(cutRs[1:2])

Filter by quality

filtFs <- file.path(path.cut, "filtered", basename(cutFs))
filtRs <- file.path(path.cut, "filtered", basename(cutRs))

out <- filterAndTrim(cutFs, filtFs, cutRs, filtRs, maxN = 0, maxEE = c(2, 2), truncQ = 2,
    minLen = 50, rm.phix = TRUE, compress = TRUE, multithread = TRUE)  # on windows, set multithread = FALSE
head(out)

Model of lear Errors

errF <- learnErrors(filtFs, multithread = TRUE)
errR <- learnErrors(filtRs, multithread = TRUE)

#plotErrors(errF, nominalQ = TRUE)

Dereplication

derepFs <- derepFastq(filtFs, verbose = TRUE)
derepRs <- derepFastq(filtRs, verbose = TRUE)

Inference of ASVs

dadaFs <- dada(derepFs, err = errF, multithread = TRUE )
dadaRs <- dada(derepRs, err = errF, multithread = TRUE )

Merge

mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose=TRUE)

Obtención de tabla de secuencias

seqtab <- makeSequenceTable(mergers)
dim(seqtab)

Quimeras removal

seqtab_nochim <- removeBimeraDenovo(seqtab, method = "consensus", multithread = T, 
                                    verbose = T)
dim(seqtab_nochim)

Denoising statistics

getN <- function(x) sum(getUniques(x))
track <- cbind(out, sapply(dadaFs, getN), sapply(dadaRs, getN), sapply(mergers, getN),
    rowSums(seqtab_nochim))
# If processing a single sample, remove the sapply calls: e.g. replace
# sapply(dadaFs, getN) with getN(dadaFs)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nonchim")
rownames(track) <- sample.names
head(track)

Sequences statistics (chatGPT)

library(tidyverse)
# Obtener los nombres de las columnas de 'df'
seqs <- colnames(seqtab)

# Función para contar letras en una cadena
contar_letras <- function(cadena) {
  str_count(cadena, "[A-Za-z]")
}

# Aplicar la función de conteo a cada elemento de 'nombres_columnas'
conteo_letras <- map_int(seqs, ~ contar_letras(.x))

#print(conteo_letras)
min(conteo_letras)
max(conteo_letras)
mean(conteo_letras)

Assign taxonomy

#----------Asignacion taxonomica-----------
ruta_clasificador <- "../ITS_corredor/HN00174206/data_REANAL_2024_LAST/sh_general_release_dynamic_18.07.2023.fasta" # de la página de UNITE
taxa <- assignTaxonomy(seqtab_nochim, ruta_clasificador, multithread = TRUE, tryRC = TRUE)

# Visualizar lo que se genero despues de la asignacion
taxa_print <- taxa
rownames(taxa_print) <- NULL

head(taxa_print)
dim(taxa_print)

Exportar objetos generados durante el pre-procesamiento

save( seqtab_nochim, taxa, 
      file = "dada2_results_paired_final.RData")
write_tsv(seqtab_final %>% rownames_to_column("#SampleID"), "table3_no_hasids.txt")


#remotes::install_github("vmikk/metagMisc")
library(metagMisc)
dada_to_fasta(seqtab = seqtab, out = "seqtab3.fasta")

seqtab_final <- as.data.frame(seqtab_nochim) %>%
  rownames_to_column(var = "ids") %>%
  mutate(ids = str_extract(ids, "[0-9]+[A-Z]+")) %>%
  column_to_rownames(var = "ids")

RESULTS FROM DIFFERENT METHODS

Statistics

ALPHA DIVERSIDAD

Figura 2. Alpha diversidad a todos los órdenes

Figura 3. Alpha diversidad por polígono al orden q=0

Figura 4. Alpha diversidad por polígono al orden q=1

Figura 5. Alpha diversidad por polígono al orden q=2

ITS WORKFLOW

Steph

20 mayo, 2024

PREPROCESSING SEQUENCES

Script to process ITS sequences from LMNP and IPNP

IMPORTING AND DEMULTIPLEXING SEQUENCES

IMPORTING TO QIIME2

DEMULTIPLEXING WITH CUTADAPT

ITSXPRESS QIIME2

OPTION 1: MERGED SEQUENCES

OPTION 2: UNMERGED SEQUENCES

ITSXPRESS STANDALONE

EXPORTING DATA

OPTION 1 : MERGED SEQUENCES

OPTION 2 : UNMERGED SEQUENCES

DADA2 AND OTUS IN QIIME2

IMPORTING STANDALONE DATA

DADA2 FOR MERGED SEQUENCES

DADA2 FOR UNMERGED SEQUENCES

DADA2 FOR UNMERGED SEQUENCES STANDALONE ISTXPRESS

OTU’s FOR MERGED SEQUENCES

DADA2 IN R

OPTION 1 : MERGED SEQUENCES

OPTION 2 : UNMERGED SEQUENCES

Exportar objetos generados durante el pre-procesamiento

RESULTS FROM DIFFERENT METHODS

Statistics

ALPHA DIVERSIDAD

PCA plots